Compression-based integral curve data reuse framework for flow visualization

Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, sourcedestination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, in order to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data. Xiaoru Yuan is the corresponding author. Fan Hong and Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University E-mail: {fan.hong, xiaoru.yuan}@pku.edu.cn Chongke Bi School of Software, Tianjin University E-mail: [email protected] Kenji Ono Research Institute for Information Technology, Kyushu University, and Advanced Institute for Computational Science, RIKEN E-mail: [email protected] Hanqi Guo Mathematics and Computer Science Division, Argonne National Laboratory E-mail: [email protected]